Sho: I recently gave a talk on data visualization at the International Conference on Infant Studies (you can find my slides, along with the other wonderful talks on power, preregistration, and ethical data peeking here on the OSF). I also played the German Cats and Dogs Scientist in the barbarplots campaign on better data visualization (on the same topic, Article 1 and Article 2 on why bar and line plots hide differences in underlying distributions). In fact, being part of the barbarplots team was my entry point into thinking more about the importance of visualizing your data in a maximally informative and honest way. Informative means finding a good balance between simplifying/summarizing and showing the underlying data structure. Honest means not (accidentally) hiding important aspects of your data. Mahiko is my office mate and – this is something I discovered while preparing the above talk – an enthusiastic data visualizer. That’s why I asked him to put together our (mostly, his) favorite data viz resources.
It has been a while, but we’re happy to present the last two parts of Page’s R course. In this lesson we will learn how to run a LMEM (linear mixed effects model). We will also introduce the packages RColorBrewer and lme4, and as always expanded your knowledge of dplyr and ggplot2 calls.
Christina, Page and I like meta-analyses. We are convinced they are a great tool to leverage past research in order to move forward: To gain an overview of the state of a field, to get an idea of research practices, to plan new experiments, and even to get novel theoretical insights.
Stephen Politzer-Ahles is Assistant Professor at the Department of Chinese and Bilingual Studies of The Hong Kong Polytechnic University. He is committed to finding solutions to current challenges in the cognitive sciences. For instance, he is developing efficient and transparent strategies to empty out his own file drawer.
p>.05. We’ve all been there. Who among us hasn’t had a student crying in our office over an experiment that failed to show a significant effect? Who among us hasn’t been that student?
Statistical nonsignificance is one of the most serious challenges facing science. When experiments aren’t p<.05, they can’t be published (because the results aren’t real), people can’t graduate, no one can get university funding to party it up at that conference in that scenic location, and in general the whole enterprise falls apart. The amount of taxpayer dollars that have been wasted on p>.05 experiments is frankly astounding. Continue reading Find a significant effect in any study
Today we’ll learn how to run an ANOVA. We also use the packages tidyr and ez to modify a data frame’s format and run ANOVAs of different types, and as always expanded our knowledge of dplyr and ggplot2 calls.
Recently, we (that is Page and Christina) successfully launched the Parisian installation of R-Ladies Global. It’s a meetup group and at the same time a non-profit coding club for all R proficiency levels, whether you’re a new or aspiring R user, or an experienced R programmer interested in mentoring, networking, and maybe picking up some new skills. We are a community designed to encourage, support and ultimately drive the development of our own R skills through a range of events, including meetups where members tackle hands-on tutorials and exercises to learn specific functionalities, informal gatherings, talks about latest trends, and debates. Our goal is to promote access to STEM (Science, Technology, Engineering, Mathematics) careers and tools for women (trans and cis) and gender-variant people. Men are welcome, too, by the way. We just need a member to bring them to the next meetup. In other words, we try to be a harassment-free zone. Sadly, that’s easier to do when men are screened beforehand.
Today we’ll learn how to take an old statistics test (logistic regression) but expand it to when you have two variables (multiple regression). The package purrr is introduced and, as always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 4.
Following up linear regression, in this lesson we’ll learn the math of logistic regression, and run a logistic regression in R. As always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 3.
Guest post by Page Piccinini
With some basics under our belt from Lesson 1, in this lesson we’ll continue working with dplyr and ggplot2, while also learning about the math behind linear regression and how to implement it in R. Plus you get to finish with a report about how the popularity of your name changes over time.
For full materials, see the course website for Lesson 2.